Jun 2022 – Sep 2022 · 3 mos · Cambridge, England, United Kingdom · On-site

• As an applied scientist intern at Amazon Web Services (AWS), I led an explorative research project focused on addressing the challenges of hyperparameter optimization for large language models (LLMs). Our primary objective was to gain a comprehensive understanding of the scaling behavior of LLMs and investigate the feasibility of extrapolating optimal hyperparameters from smaller LLMs to their massive counterparts. This hands-on work involved orchestrating the parallel training of multiple LLMs from scratch across numerous GPU cloud instances to gain insights into their scaling dynamics.

Oct 2021 – Apr 2022 · 6 mos · Cambridge, England, United Kingdom · On-site

• As a student researcher at Secondmind (formerly Prowler.io), a research-intensive AI startup renowned for its innovations in Bayesian optimization (BO) and Gaussian processes (GPs), I contributed impactful research and open-source code aligned with their focus on advancing probabilistic ML. Specifically, I developed open-source software to facilitate sampling efficiently from GPs, substantially improving their accessibility and functionality. Additionally, I led a research initiative to improve the integration of neural networks (NNs) with GP approximations, bridging a critical gap between probabilistic methods and deep learning. These efforts culminated in a research paper that was selected for an oral presentation at the International Conference on Machine Learning (ICML).

Jun 2019 – Dec 2019 · 6 mos · Berlin, Germany · On-site

• As an applied scientist intern at Amazon Web Services (AWS), I contributed to the development of the Automatic Model Tuning functionality in AWS SageMaker. My primary focus was on advancing AutoML and hyperparameter optimization, particularly Bayesian optimization (BO) methods. I spearheaded a research project aimed at integrating multi-fidelity BO with asynchronous parallelism to significantly improve the efficiency and scalability of model tuning. This initiative led to the development of a research paper and the release of open-source code within the AutoGluon library, subsequently forming the basis of the SyneTune library.

Jun 2016 – Apr 2019 · 2 yrs 10 mos · Greater Sydney Area · On-site

• As a machine learning (ML) software engineer at Data61, I was an integral part of the Inference Systems Engineering Team, specializing in probabilistic ML for diverse problem domains. Our focus encompassed areas such as spatial inference and Bayesian experimental design, with a primary emphasis on scalability. I led the development of new microservices and contributed to the development of open-source libraries for large-scale Bayesian deep learning. I also had a stint with the Graph Analytics Engineering Team, where my contributions to research on graph representation learning led to a research paper selected for a spotlight presentation at the Conference on Neural Information Processing Systems (NeurIPS).

Jun 2015 – Jun 2016 · 1 yr · Greater Sydney Area · On-site

• As a machine learning (ML) software engineer at NICTA, I was part of an interdisciplinary ML research team contributing to the Big Data Knowledge Discovery initiative, which engaged with leading scientists across various natural sciences domains to develop Bayesian ML software frameworks to support Australia’s evolving scientific research landscape. During this time, I led the development and release of numerous open-source libraries for applying Bayesian ML at scale.

Nov 2013 – Feb 2014 · 3 mos · Sydney, New South Wales, Australia

• As a summer vacation scholar at CSIRO's Language and Social Computing team, I applied cutting-edge machine learning (ML) and natural language processing (NLP) techniques to build a robust text classification system for automated sentiment analysis.